import torch
import torch.nn as nn
import torch.nn.functional as F

class BasicBlock(nn.Module):
    expansion = 4
    def __init__(self, in_channels, out_channels, stride = 1, downsample = None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size = 1, bias = False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size = 3, stride = stride, padding = 1, bias = False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size = 1, bias = False)
        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
        self.downsample = downsample
        self.relu = nn.ReLU(inplace = True)
        
    def forward(self, x):
        temp = x
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        if self.downsample is not None:
            temp = self.downsample(x)
        out += temp
        out = self.relu(out)
        return out
    
class ResNet_50(nn.Module):
    def __init__(self, num_classes = 100):
        super(ResNet_50, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size = 7, stride = 2, padding = 3, bias = False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace = True)
        self.maxpool = nn.MaxPool2d(kernel_size = 3, stride = 2, padding = 1)
        self.layer1 = self._make_layer(64, 3, stride = 1)
        self.layer2 = self._make_layer(128, 4, stride = 2)
        self.layer3 = self._make_layer(256, 6, stride = 2)
        self.layer4 = self._make_layer(512, 3, stride = 2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * BasicBlock.expansion, num_classes)
        self._init_weight()
        
    def _make_layer(self, out_channels, block_nums, stride):
        downsample = None
        if stride != 1 or self.in_channels != out_channels * BasicBlock.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels * BasicBlock.expansion, kernel_size = 1, stride = stride, bias = False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )
        layers = []
        layers.append(BasicBlock(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels * BasicBlock.expansion
        
        for _ in range(1, block_nums):
            layers.append(BasicBlock(self.in_channels, out_channels))
            
        return nn.Sequential(*layers)
    
    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode = 'fan_out', nonlinearity = 'relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
                
    def forward(self, x):
        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x     